@import tool.ReportPojo.ReportData
@(resultPath: String, i: Int)(implicit data: ReportData)
<p class="paragraph">
	Differential metabolites from Potential_Biomarker section will be further screened for prediction and diagnosis.
</p>
<p class="paragraph">
	Receiver operating characteristic curve(ROC) for each single differential metabolite is executed. Area Under Curve(AUC) of each metabolite is stored in
	<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/04_Diagnose_Model_Generation/Logistic_Regression/Single_Met_ROC.csv">
		Single_Met_ROC.csv
	</a>
	<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/04_Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>

	. ROC plot of each single metabolite is shown in
	<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/04_Diagnose_Model_Generation/Logistic_Regression/Single_Met_ROC.pdf" target="_blank">
		Single_Met_ROC.pdf
	</a>
	<a href="@(resultPath)Treatment/@(data.treats(i))/@(data.resultDatas(i).diagnoseDir)/04_Diagnose_Model_Generation/Logistic_Regression" target="_blank"><span class="fa fa-folder-open"></span></a>
	.
</p>
<p class="paragraph">
	In order to find out candidate biomarkers from differential metabolites, we carried out Random Forest(RF), Support Vector Machine(SVM) and Boruta analysis in sequence.
</p>
<p class="paragraph">
	The Boruta algorithm is a wrapper built around the random forest classification algorithm. It tries to capture all the important, interesting features you might have in your dataset with respect to an outcome variable. Maximum iteration time is 1000. At every iteration, the algorithm compares the Z-scores of the shuffled copies of the features and the original features to see if the latter performed better than the former. If it does, the algorithm will mark the feature as important. In essence, the algorithm is trying to validate the importance of one feature by comparing with random shuffled copies, which increases the robustness. This is done by simply comparing the number of times a feature did better with the shadow features using a binomial distribution. Metabolites labeled as “Confirmed” will be used for model building and prediction. ROC and Precision Recall (PR) Curve will be plotted for model evaluation.
</p>

